post-transcriptional methylation removal in rnas using alkb demethylase and subsequent rna-cdna hybrid hydrolysis in rna sequencing preparation

Análisis de la dinámica del ARNt mediante AQRNA-seq

Next-generation sequencing (NGS), also known as massively parallel sequencing, is a DNA sequencing technology that involves DNA fragmentation, ligation of adaptor oligonucleotides, polymerase chain reaction (PCR)-based amplification, sequencing of the DNA, and reassembly of the fragment sequences into a genome. The adaptation of NGS to sequence RNA (RNA-seq) is a powerful approach to identify and quantify RNA transcripts and their variants1. Innovative developments in RNA library preparation workflows and bioinformatic analysis pipelines, coupled with advancements in laboratory instrumentation, have expanded the repertoire of RNA-seq applications, progressing beyond exome sequencing into advanced functional omics like non-coding RNA profiling2, single cell analysis3, spatial transcriptomics4,5, alternative splicing analysis6, among others. These advanced RNA-seq methods reveal complex RNA functions through quantitative analysis of the transcriptome in normal and diseased cells and tissue.
Despite these advances in RNA-seq, several key technical features limit the quantitative power of the method. While most RNA-seq methods allow precise and accurate quantification of changes in the levels of RNAs between experimental variables (i.e., biological samples and/or physiological states), they cannot provide quantitative comparisons of the levels of RNA molecules within a sample. For example, most RNA-seq methods cannot accurately quantify the relative number of copies of individual tRNA isoacceptor molecules in a cellular pool of expressed tRNAs. As highlighted in the companion publication7, this limitation to RNA-seq arises from several features of RNA structure and the biochemistry of library preparation. For example, the activity of the ligation enzymes used to attach the 3&#39;- and 5&#39;-end sequencing linkers to RNA molecules is strongly influenced by the identity of the terminal nucleotides of the RNA and the sequencing linkers. This leads to large variations in efficiencies of linker ligations and profound artifactual increases in sequencing reads8,9,10.
A second set of limitations arise from the inherent structural properties of RNA molecules. Specifically, RNA secondary structure formation and dynamic changes in the dozens of post-transcriptional RNA modifications of the epitranscriptome&#160;can cause polymerase fall-off or mutation during reverse transcription. These errors result in incomplete or truncated cDNA synthesis or altered RNA sequence. While both of these phenomena can be exploited to map secondary structures or some modifications, they degrade the quantitative accuracy of RNA-seq if subsequent library preparation steps fail to capture truncated cDNAs or if data processing throws out mutated sequences not matching a reference dataset11,12. Furthermore, the immense chemical, length, and structural diversity of RNA transcripts, as well as the lack of tools to uniformly fragment long RNAs, diminishes the applicability of most RNA-seq methods to all RNA species13.
The AQRNA-seq (absolute quantification RNA sequencing) method has been developed to remove several of these technical and biological constraints that limit quantitative accuracy7. By minimizing sequence-dependent biases in capture, ligation, and amplification during RNA sequencing library preparation, AQRNA-seq achieves superior linearity compared to other methods, accurately quantifying 75% of a reference library of 963 miRNAs within 2-fold accuracy. This linear correlation of sequencing read count and RNA abundance is also observed in an analysis of a variable length pool of RNA oligonucleotide standards and in reference to orthogonal methods like northern blotting. Establishing linearity between sequencing read count and RNA abundance enables AQRNA-seq to achieve accurate, absolute quantification of all RNA species within a sample.
Here is a description of the protocol for AQRNA-seq library preparation workflow and the accompanying downstream data analytics pipeline. The method was applied to elucidate the dynamics of tRNA abundance during starvation-induced dormancy and subsequent resuscitation in&#160;the Mycobacterium bovis bacilli de Calmette et Gu&#233;rin (BCG) model of tuberculosis. Results were presented for the exploratory visualization of the sequencing data, along with subsequent clustering and differential expression analyses that unveiled discernable patterns in tRNA abundance associated with various phenotypes.

Autor destacado: El papel de AQRNA-seq en el mapeo de ARN pequeños y el desentrañamiento de los mecanismos de traducción de proteínas

AQRNA-seq para cuantificar ARNs pequeños

AQRNA sequencing quantitatively maps small RNAs such as microRNAs, tRNAs, and tRNA fragments in virtually any cell or tissue sample. It can be used to map some of the 170 known tRNA modifications, but there are other methods that are more specific for mapping. You can use AQRNA-seq to discover disease biomarkers in the RNome and explore protein translation mechanisms at the systems level.Most RNA-seq methods can't accurately quantify the abundance of individual RNA molecules in a sample. This is caused by both structural properties of the RNA, such as secondary structures, post transcriptional modifications, as well as the biochemistry of library preparation such as ligation biases of a thousand fold induced by the terminal nucleotides on the RNAs. AQRNA-seq was developed to overcome several technical and biological challenges, limiting the accurate quantification of RNA abundance in the sample.Compared to other matters, it achieves linearity between recounts and copy numbers of RNA molecules, accurately, it quantifies 75%of a reference library, compressing 963 microRNAs with twofold accuracy. AQRNA-seq is unique in providing absolute quantification of small RNAs. This links the sequencing recounts directly to the number of copies of an RNA molecule in the sample.This level of accuracy is crucial for comparing dozens to hundreds of tRNAs in a sample, and it's unlike regular small RNA-seq, which only permits relative quantification across conditions and samples. We designed AQRNA-seq for an unmet need in understanding protein translation. We found that cells reprogrammed dozens of tRNA modifications in the number of copies of the modified tRNAs, to allow selective translation of messenger RNAs that are enriched with codons matching those tRNAs.AQRNA-seq allows us to quantify changes in the tRNA pool as part of this mechanism. We've used it extensively to validate this new model for protein translation.

Eliminación de la metilación postranscripcional en ARN mediante AlkB desmetilasa y posterior hidrólisis híbrida ARN-ADNc en la preparación de la secuenciación del ARN

Para comenzar, prepare 10 mililitros de tampón de reacción 2X AlkB y esterilicelo a través de un filtro de jeringa de 0,2 micras. En un tubo de PCR estéril, agregue 20 microlitros de los ARN ligados al enlazador 1, 50 microlitros del tampón de reacción 2X AlkB, dos microlitros de la AlkB desmetilasa, un microlitro de inhibidor de ARN y 27 microlitros de ARN de agua libre para preparar la reacción de digestión de AlkB. Incubar la mezcla de digestión AlkB a temperatura ambiente durante dos horas para eliminar la metilación postranscripcional de los ARN.Para una separación de fase limpia, agregue 50 microlitros de agua libre de ARN en la reacción AlkB. Luego, agregue 100 microlitros de mezcla de fenol, cloroformo y alcohol isoamílico. Agite el tubo durante 10 segundos.Centrifugar la mezcla de AlkB a 16.000 g durante 10 minutos. Transfiera aproximadamente 140 microlitros de los ARN que contienen la capa acuosa superior a un tubo estéril de 1,5 mililitros. Para eliminar el fenol residual, agregue 100 microlitros de cloroformo a los ARN extraídos y agite el tubo.Centrifugar la mezcla a 16.000 g durante 10 minutos y transferir aproximadamente 120 microlitros de la capa acuosa superior a un tubo estéril de 1,5 mililitros. Después de la reacción de transcripción inversa, agregue un microlitro de hidróxido de sodio de cinco molares en el híbrido de ADNc de ARN. Incubar a 93 grados centígrados durante tres minutos para hidrolizar la cadena de ARN del híbrido de ARNc.A continuación, añade 0,77 microlitros de ácido clorhídrico de cinco molares para neutralizar la reacción. Prepare un gel de agarosa al 3% en tampón TAE. Mezcle un microlitro de colorante de carga 6X en cinco microlitros de producto PCR y cargue el gel con la mezcla.Cargue cinco microlitros de 50 o 100 escalas de ADN de pares de bases en el pocillo antes de la primera muestra y en el pocillo después de la última muestra. Ejecute la electroforesis en gel a 120 voltios y 400 miliamperios durante 75 minutos para localizar los productos de PCR. Después de la electroforesis, coloque el gel en una caja y llénela con agua desionizada hasta que el gel esté completamente sumergido.Agregue 10 microlitros de bromuro de etidio en el agua. Envuelva la caja con papel de aluminio y manche durante 30 minutos en una coctelera. Deseche el agua que contiene bromuro de etidio en una botella de desecho colocada en una campana extractora.Enjuague el gel con agua desionizada una vez y deseche el agua en una botella de desecho. Después de lavar el gel con agua desionizada durante 10 minutos, use un generador de imágenes en gel para visualizar las bandas y adquirir una imagen de alta resolución del gel.

Research

JoVE Journal

Genetics

Post-Transcriptional Methylation Removal in RNAs Using AlkB Demethylase and Subsequent RNA-cDNA Hybrid Hydrolysis in RNA Sequencing Preparation

Analyzing tRNA Dynamics Using AQRNA-seq

AQRNA-seq for Quantifying Small RNAs

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

Investigación

Genética